Proportional, integral, and derivative (PID) controllers are commonly used for feedback control of dynamic systems, such as for regulating temperature in chemical processes and speed in servo motors. PID controllers provide three gain values: proportional gain, derivative gain, and integral gain. Good closed-loop performance of a controlled system is achieved by appropriate selection of these gain values, which are functions of the system's input-output tranfer function.
In the prior art, PID tuners incorporate either conventional control theory or intelligent control techniques, such as expert systems and fuzzy logic. Conventional control theory requires a priori knowledge of the dynamics of the controlled system. In many real-world systems, however, the controlled parameters change over time as a result of factors such as frictional wear, aging of components, and variations in temperature and pressure. In such time-varying or nonlinear processes, traditional non-adaptive control techniques do not correct for variations in system parameters. As a result, system response degrades with time because control is based on a predetermined model of the system that becomes outdated. Response degradation is particularly undesirable for systems that must perform reliably under extreme environmental conditions, or with high duty cycles, or where manual adjustments may not be immediately available.
Adaptive control techniques based on conventional control theory are well-known for tuning slowly varying systems where the coefficients of the system transfer function remain within prescribed bounds. However, conventional control theory can not provide stable tuning algorithms to compensate for changes in the order or structure of the system model caused, for example, by excitation of unmodeled modes in the dynamic system. On the other hand, control techniques based on expert systems can require a large number of rules for precise tuning under various changes in the system dynamics.
Fuzzy logic has also been applied to PID controller systems, as described in U.S. Pat. No. 4,903,192 issued to Saito et al. Fuzzy inferencing is based on a set of rules or heuristics, as in expert systems, but it also provides the ability to interpolate between the rules. This is important because a fundamental problem for PID tuners is the deleterious effect of incorrect rules or heuristics. A rule or heuristic may be correct initially, but as the system dynamics change over time the same rule or heuristic may become invalid or incorrect. Therefore, to maintain a desired level of performance in time-varying and nonlinear systems, the PID controller gains must be adapted to changes in the system parameters.
In prior art PID tuners, no provision has been made for checking the validity of the heuristics during operation. As a result, the fixed input-output mapping used by prior art fuzzy logic or expert systems is effective for tuning only a small class of time-varying dynamic systems. A capability for stable and effective tuning of a large class of dynamic systems, however, requires self-monitoring and on-line adaptation to unmodeled system modes. Thus, there is a need for an automatic tuner that continuously monitors the input and output of the controlled system, determines the appropriate gain values of the PID controller, and provides on-line compensation for parameter variations or model changes as they occur in the system.